Along with development of semiconductor technology, a calculating capacity of hardware is rapidly enhanced, and processing time of various big data is gradually reduced. On this basis, artificial neural network technology is also further improved. In particular, such artificial neural network characterized by calculation intensity as deep learning is gradually largely applied in the fields such as computer vision, image and video recognition, and natural language processing.
Meanwhile, the scale of such artificial neural network as the deep learning is in constant growth, and comparatively advanced links of the neural networks currently published have reached hundreds of millions. Thus, a general-purpose processor (CPU) or a graphics processor (GPU) currently adopted in a calculating system to achieve the calculation of the artificial neural network will face the following challenges: along with a gradual approach of a transistor circuit to a limit, the Moore's law will come to an end, and a performance enhancement is no longer significant. A simple enhancement of a calculation performance has been gradually unable to meet gradually increasing requirements for the calculation of the artificial neural network. A deep learning processor born for this reason becomes a feasible solution, and shows a great potential.
The deep learning processor is a dedicated calculating device designed with respect to operation characteristics of a compressed artificial neural network, and is connected to the current calculating system as an external calculating device, and the compressed artificial neural network is configured in the deep learning processor in the form of initialized parameters. Thus, the deep learning processor can perform calculations of input data according to the configured neural network, and obtain a final calculation result rapidly and effectively.
The deep learning processor serves as a circuit calculating device, and the accuracy of the calculation result thereof naturally should be ensured. But since various interferences exist in an electronic system, in addition to making security in terms of a hardware design, it is further required to further enhance stability thereof by means of a good monitoring mechanism. But as mentioned above, the deep learning processor is the dedicated calculating device designed with respect to the operation characteristics of the compressed artificial neural network, the data processed thereby have correlations, and the previous method of adding a check bit to data cannot be used to check whether a calculating structure of the deep learning processor is accurate. Thus, there is a difficulty to well monitor the deep learning processor during its running. But if the deep learning processor cannot be well monitored, the abnormality of the deep learning processor cannot be found timely and accurately, so that the calculation result of the whole calculating system is inaccurate.